Real-time AI Genome Processing - Powered by Groq

Groq
10 Apr 202405:25

TLDRIn the medical field, accuracy and reliability are crucial, which poses a challenge for the use of Large Language Models (LLMs) due to their tendency to generate incorrect information. To address this, Peter demonstrates the use of Retrieval-Augmented Generation (RAG), a technique that enhances LLMs by incorporating additional information from a database of text sources. This method not only reduces inaccuracies but also provides referenced responses, which are essential for medical professionals. Using a vector database with 3000 medical genomics abstracts from pharmGKB, Peter showcases two demos: one illustrating the difference between LLM outputs with and without RAG, highlighting the increased detail and provision of working PubMed links in the latter; and another demonstrating real-time genome annotation using an LLM with RAG, turning someone's DNA into readable information. These demonstrations effectively illustrate the potential of LLMs in a medical context when augmented with RAG for improved reliability and accuracy.

Takeaways

  • 🧬 The importance of reliability and accuracy in medical contexts is highlighted, as generative LLMs (Large Language Models) often hallucinate or make up information.
  • 🚫 The slow adoption of generative LLMs in medicine due to their tendency to generate incorrect information.
  • 🔍 Doctors and medical professionals require referenced or sourced information to verify the data provided by LLMs.
  • 📚 RAG (Retrieval Augmented Generation) is introduced as a technique to enhance the accuracy and reliability of LLMs by using additional information surrounding a query.
  • 🔗 RAG allows LLMs to give referenced responses by feeding them top sources from a database of text sources.
  • 💡 The demonstration showcases the use of a vector database containing 3000 medical genomics abstracts from pharmGKB, a database for gene-drug interactions.
  • 🆚 A comparison of LLM outputs with and without RAG is presented, showing the difference in detail and the inclusion of working PubMed links with RAG.
  • 🧬 The second demo involves using an LLM (Llama-2 70 billion) on Groq hardware with RAG to annotate the genome in real time.
  • 🧬 VCF (Variant Calling Format) is explained as a sequence genome that shows which variants of each gene a person has.
  • 🔬 The process of iteratively processing the VCF file, searching for matches in the gene-drug database, and summarizing the abstracts with working PubMed links is described.
  • ⚡ The speed at which Groq hardware can make someone's DNA readable in real time is emphasized.
  • 🌟 The potential for LLMs in medical settings is acknowledged, provided that reliability and accuracy are improved with techniques like RAG.

Q & A

  • What is the primary concern with using LLMs in a medical context?

    -The primary concern is the tendency of LLMs to hallucinate or make up information, which can be highly problematic in a medical context where reliability and accuracy are paramount.

  • What is the role of referenced or sourced information in the medical field?

    -Referenced or sourced information is crucial for doctors and medical professionals as it allows them to verify the information and ensure its accuracy before making any medical decisions.

  • What technique is used to enhance the accuracy and reliability of LLMs in medical contexts?

    -Retrieval Augmented Generation (RAG) is used to enhance the output accuracy and reliability of LLMs by incorporating additional information surrounding a query into the input to the LLM.

  • How does RAG work in the context of LLMs?

    -RAG works by indexing a database of text sources and gathering relevant sources for a user query. The top sources are then fed into the LLM, which answers the user query based on these sources, reducing hallucinations and providing referenced responses.

  • What type of database is used in the demos to enhance the LLM's output?

    -A vector database containing 3000 medical genomics abstracts sourced from pharmGKB, which includes clinical guidelines for gene-drug interactions, is used in the demos.

  • What is the significance of the working PubMed links in the output with RAG?

    -The working PubMed links in the output with RAG allow doctors to verify the information provided by the LLM, ensuring that they can check the source of the data and be confident in its accuracy.

  • What is the purpose of using an LLM to annotate the genome?

    -The purpose of using an LLM to annotate the genome is to provide real-time analysis and summaries of gene variants, making the complex genomic data more accessible and understandable for medical professionals.

  • How does the LLM process the VCF file?

    -The LLM processes the VCF file by iteratively searching for matches in the gene-drug database. When a match is found, the LLM summarizes the relevant abstract and provides a working PubMed link.

  • What hardware is used to run the LLM in the demos?

    -The LLM in the demos is run on Groq hardware, with interactions managed through an API.

  • What is the significance of the real-time processing of DNA using Groq hardware?

    -The real-time processing of DNA using Groq hardware demonstrates the potential for LLMs to make genomic data immediately readable and understandable, which can be highly beneficial in medical settings.

  • How does the use of RAG with LLMs in medical settings address the issue of reliability and accuracy?

    -By providing the LLM with additional context and referenced sources, RAG helps to reduce the likelihood of the LLM generating inaccurate or hallucinated information, thus improving the reliability and accuracy of the LLM's output in a medical context.

  • What is the potential impact of using LLMs with RAG in the medical field?

    -The use of LLMs with RAG in the medical field can lead to more accurate and reliable medical information, potentially improving patient care and enabling faster and more informed decision-making by medical professionals.

Outlines

00:00

🧬 Introduction to Medical Demos with Groq Powered LLMs

Peter introduces himself and the purpose of the video: to demonstrate medical demos using Groq-powered LLMs. He emphasizes the importance of reliability and accuracy in medicine and acknowledges the issue of LLMs generating false information, known as 'hallucinations'. Peter explains that this has led to slow adoption of LLMs in the medical field. He also mentions the need for doctors to have referenced or sourced information. To address these issues, Peter introduces the concept of Retrieval Augmented Generation (RAG), a technique to enhance the accuracy and reliability of LLMs by providing them with additional information surrounding a query. He explains how RAG works by indexing a database of text sources and feeding the most relevant ones into the LLM to generate responses. The demo will use a vector database containing 3000 medical genomics abstracts sourced from pharmGKB, a database of clinical guidelines for gene-drug interactions.

🔬 RAG Demo: Gene Variants and Warfarin Doses

Peter presents the first demo, which illustrates the classic use of RAG. The demo involves connecting a genomics database to an LLM named Mixtral, running on Groq hardware. For demonstration purposes, Peter inputs a query about gene variants associated with adjusted warfarin doses. The output is shown both with and without RAG. The RAG output provides more detailed information, including missing gene variants and genes, compared to the output without RAG. Most importantly, the RAG output contains working PubMed links, allowing doctors to verify the information. Peter highlights the significant difference in output quality between using RAG and not using it, and how RAG can customize LLMs for medical applications.

🧬 Real-time Genome Annotation with LLMs and RAG

In the next demo, Peter discusses the challenge of using LLMs to annotate the genome, which is typically a slow and difficult process. However, he demonstrates how Llama-2, a 70 billion-parameter LLM running on Groq hardware with RAG, can annotate the genome in real time. Peter provides context by explaining VCF (variant calling format) as a sequence genome that shows a person's gene variants. An example VCF file is uploaded and rendered, with emphasis on the ID column that describes gene variants. The process involves iteratively processing the VCF, searching for matches in the gene-drug database, and summarizing relevant abstracts using the LLM. Working PubMed links are provided for each match. The result is a real-time annotation of someone's DNA, showcasing the potential of LLMs in medical settings when combined with techniques like RAG.

📘 Conclusion: The Role of LLMs in Medicine with RAG

Peter concludes the presentation by reiterating the potential of LLMs in medical settings, provided their reliability and accuracy are improved through techniques like RAG. He thanks the audience for their attention and highlights the successful demonstration of LLMs' capabilities in medical contexts when augmented with RAG.

Mindmap

Keywords

💡Groq

Groq is a company that specializes in developing high-performance AI accelerators. In the context of the video, Groq hardware is used to run the LLM (Large Language Model), which is crucial for processing medical genomics data in real-time. The use of Groq hardware allows for the efficient execution of complex AI algorithms, which is vital for the medical demos presented.

💡LLMs (Large Language Models)

Large Language Models (LLMs) are advanced AI systems designed to understand and generate human-like text. They are a core component in the medical demos, where they are used to process and provide information about gene variants and genomics. However, they are known to sometimes 'hallucinate' or generate incorrect information, which is a significant concern in a medical context.

💡RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) is a technique that enhances the accuracy and reliability of LLMs by incorporating external, referenced information into their responses. In the video, RAG is used to provide doctors with sourced information, reducing the risk of misinformation and allowing for verification of the data presented.

💡Medical Genomics Abstracts

Medical genomics abstracts are summaries of scientific research related to genomics, the study of genes and their functions. In the script, a vector database containing 3000 medical genomics abstracts sourced from pharmGKB is used to provide the LLM with relevant information to answer queries accurately.

💡pharmGKB

pharmGKB is a comprehensive database that provides knowledge about the impact of genetic variation on drug response. It is mentioned in the script as the source of medical abstracts used in the vector database for the RAG technique, which is crucial for understanding gene-drug interactions.

💡VCF (Variant Calling Format)

The Variant Calling Format (VCF) is a file format used in genomics to store information about genetic variants. In the video, an example VCF file is uploaded to demonstrate how the LLM, with the help of RAG, can annotate the genome in real-time, making it more accessible and understandable.

💡Annotation

In the context of genomics, annotation refers to the process of adding information to the DNA sequence to describe the function of genes and other genomic elements. The video showcases how an LLM can be used to annotate the genome, providing summaries and links to relevant medical abstracts.

💡Real-time Processing

Real-time processing is the ability of a system to handle input and produce output immediately as it is received, without significant delay. The video demonstrates the use of Groq hardware to enable real-time processing of genomics data, which is essential for quick and efficient medical analysis.

💡Pubmed Links

Pubmed is a database of biomedical literature, and in the video, it is mentioned as a source of verified information. The LLM with RAG provides working Pubmed links alongside its responses, allowing medical professionals to verify the information and ensuring the reliability of the data.

💡Customization

Customization in the context of the video refers to the process of adapting the LLM to suit specific needs, such as the medical context. By using RAG, the LLM can be tailored to provide more accurate and reliable information, making it a useful tool for medical professionals.

💡Reliability and Accuracy

Reliability and accuracy are critical factors in the medical field, where the correctness of information can have significant implications. The video emphasizes the importance of these factors and how techniques like RAG can enhance the reliability and accuracy of LLMs when used in a medical setting.

Highlights

Peter is demonstrating medical applications using Groq powered LLMs (Large Language Models).

In medicine, reliability and accuracy are crucial, but LLMs often hallucinate or fabricate information.

The slow adoption of generative LLMs in medicine due to concerns about the accuracy of information.

Medical professionals require referenced or sourced information to verify LLM outputs.

RAG (Retrieval Augmented Generation) is introduced as a technique to enhance LLM output accuracy.

RAG reduces hallucinations in LLMs by using a database of text sources to inform responses.

The process of RAG involves indexing a database and feeding top sources into the LLM for user query responses.

A vector database with 3000 medical genomics abstracts from pharmGKB is used in the demos.

The first demo showcases the genomics database connected to the LLM Mixtral running on Groq hardware.

Comparison of LLM outputs with and without RAG, highlighting the increased detail and provision of working PubMed links with RAG.

The second demo involves real-time genome annotation using LLM Llama-2 70 billion on Groq hardware with RAG.

VCF (variant calling format) is explained as a sequence genome showing a person's gene variants.

The process of iteratively processing VCF files to match gene-drug interactions and summarizing with LLM.

Real-time generation of genome annotations with working PubMed links, making DNA readable.

The potential of LLMs in medical settings is emphasized, provided reliability and accuracy are improved with techniques like RAG.

The presentation concludes by highlighting the successful integration of LLMs in medical applications through RAG.

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